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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Gan, Xu Huang, Chongwen Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Hangzhou China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Southeast University National Mobile Communications Research Laboratory Nanjing China

ISBN: (纸本)9798350351255

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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Bio-inspired Relocalization for Indoor Robots in Visual Ambiguous Scenarios 2

Bio-inspired Relocalization for Indoor Robots in Visual Ambi...

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2nd International Conference on Consumer Electronics and computer engineering, ICCECE 2022

作者： Long, Yixuan Jiang, Rui Ye, Fang College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology China National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore

ISBN: (纸本)9781665408868

In recent years, robots have been widely used in the service industry to improve work efficiency. An indoor service robot should have long-term autonomous adaptability and this is achieved by performing lifelong simultaneous localization and mapping (SLAM). However, when a robot wakes up to SLAM, it needs to relocate itself first. In real-world applications, the visual ambiguous environment which contains multiple locations with similar appearances or features is a challenging scenario for localization. Considering the insufficient expression ability of a single sensor, this paper proposes a bio-inspired relocalization method to deal with this problem. The local view cells model maintains multi-hypotheses to provide visual-based coarse relocalization. Then, an obstacle cell model converts the self-centered lidar information into allocentric representation. And it serves as the fine relocalization. A continuous attractor neural network (CANN) is applied to integrate the candidates obtained through the above two stages. Finally, the result of relocalization is selected by a double-checking mechanism. In the experiments, the success rate of the proposed method reaches 95 % in total and 75% in the most challenging scene. The translation error in all scenes is less than 0.15m. © 2022 IEEE.

关键词： Robots

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Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning

arXiv

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arXiv 2024年

作者： Li, Yuanman He, Yingjie Chen, Changsheng Dong, Li Li, Bin Zhou, Jiantao Li, Xia Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Computer Science Ningbo University Ningbo China Department of Computer and Information Science University of Macau Macau China

Recent advances in deep learning algorithms have shown impressive progress in image copy-move forgery detection (CMFD). However, these algorithms lack generalizability in practical scenarios where the copied regions are not present in the training images, or the cloned regions are part of the background. Additionally, these algorithms utilize convolution operations to distinguish source and target regions, leading to unsatisfactory results when the target regions blend well with the background. To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods. Specifically, the study develops a deep cross-scale PatchMatch (PM) method that is customized for CMFD to locate copy-move regions. Unlike existing deep models, our approach utilizes features extracted from high-resolution scales to seek explicit and reliable point-to-point matching between source and target regions. Furthermore, we propose a novel pairwise rank learning framework to separate source and target regions. By leveraging the strong prior of point-to-point matches, the framework can identify subtle differences and effectively discriminate between source and target regions, even when the target regions blend well with the background. Our framework is fully differentiable and can be trained end-to-end. Comprehensive experimental results highlight the remarkable generalizability of our scheme across various copy-move scenarios, significantly outperforming existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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Enhanced mechanical performance of gradient-structured CoCrFeMnNi high-entropy alloys induced by industrial shot-blasting

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Rare Metals 2023年第3期42卷 982-993页

作者： Ming-Zhi Zhang Kun Zhang Kai-Kai Song Xiao-Yu Zou Wei-Dong Song Ke-Feng Li Li-Na Hu Ze-Qun Zhang Jürgen Eckert Shenzhen Research Institute of Shandong University Shenzhen 518057China CAS Key Laboratory of Microgravity(National Microgravity Laboratory) Institute of MechanicsChinese Academy of SciencesBeijing100190China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Guangdong Institute of Materials and Processing Guangdong Academy of SciencesGuangzhou 510650China Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) Shandong UniversityJinan 250061China Erich Schmid Institute of Materials Science Austrian Academy of SciencesLeoben 8700Austria Department of Materials Science Montanuniversitat LeobenLeoben 8700Austria

In this study,CoCrFeMnNi high-entropy alloys(HEAs)with a surface gradient nanostructure were produced using industrial shot blasting,which improved their mechanical properties compared to the untreated *** severely plastically deformed(SPD)surface layer had a multi-scale hierarchical structure with a high density of stacking faults,deformation nanotwins,and amorphous *** depth of the SPD layer steadily increased as the shot-blasting time *** differences in the microhardness and tensile strength before and after shotblasting demonstrated the significant effect of the SPD layer on the mechanical *** microhardness of the homogenized HEA was~5 *** comparison,the maximum microhardness of the specimens after 20 min of shot blasting was~8.0 GPa at the *** yield strength also improved by 178%,and a large ductility of~36%was *** nanograin boundary,stacking fault,and twin strengthening within the gradientnanostructured surface layer caused the strength to *** tensile deformation,strain concentration began at the surface of the specimen and gradually spread to the ***,the gradient-nanostructured surface layer with improved strain hardening can prevent early necking and ensure steady plastic deformation so that high toughness is achieved.

关键词： High-entropy alloy Shot blasting Gradient structure Mechanical property Deformation mechanism

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Enhanced Breast Cancer Molecular Biomarker Classification: A Novel Two-Stage Machine Learning Pipeline for Accurate Histological Analysis of Whole Slide Images

Enhanced Breast Cancer Molecular Biomarker Classification: A...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Aboudessouki, A. Ali, Kh.M. Alksas, A. Elsharkawy, M. Rahma, M. Abo Ghazal, M. Mekky, N. El-Daydamony, E. Gondim, D. El-Baz, A. BioImaging Laboratory Bioengineering Department University of Louisville United States Pathology Department Faculty of Medicine Mansoura University Egypt Mansoura University Hospital Mansoura University Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University United Arab Emirates Department of Information Technology Faculty of Computers and Information Mansoura University Egypt Department of Pathology and Laboratory Medicine University of Louisville LouisvilleKY United States

ISBN: (纸本)9798350368741

Breast cancer, a prevalent and diverse form of cancer, is characterized by unique clinicopathologic features. Accurate classification of its molecular subtype, essential for targeted treatment and improved survival rates, relies on testing molecular biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 antigen. This study introduces an innovative two-stage machine learning pipeline for breast cancer PR molecular biomarker classification using whole slide immunohistochemistry images. Our approach consists of two specialized stages. In the first stage, a watershed algorithm segments the Cells from the input image. Morphological and statistical features extracted from these segments are then used by machine learning classifiers to distinguish between tumor and nontumor tissues [1]-[3]. The second stage focuses on the tumor tissues that were identified in the first stage, extracting texture and appearance features to classify them into positive or negative biomarker responses, again using machine learning classifiers [4], [5]. This method not only automates the classification of PR biomarkers in tumor regions but also generates a detailed image map for each cell in the Whole Slide Image (WSI). By doing so, it can determine whether the tissue represents tumor cells and whether the tumor tissue exhibits positive or negative PR status. The results show the ability of our machine learning-based approach to augment pathologists' diagnostic capabilities, offering significant advancements in the automated classification of histopathological images. © 2025 IEEE.

关键词： Breast cancer Histopathology Immunohistochemistry Machine Learning Marker-based Watershed Segmentation Molecular biomarker Status Molecular biomarkers Tissue classification Tumor Detection Watershed Segmentation

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Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI

Self-supervised Contrastive Learning for 6G UM-MIMO THz Comm...

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IEEE International Conference on Communications Workshops, ICC

作者： Rafid Umayer Murshed Md Saheed Ullah Mohammad Saquib Moe Z. Win Department of Electrical and Computer Engineering The University of Texas Dallas Texas USA Department of Electrical and Computer Engineering University of Delaware Delaware USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Massachusetts USA

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

This paper investigates the potential of contrastive learning in 6G ultra-massive multiple-input multiple-output (UM-MIMO) communication systems, specifically focusing on hybrid beamforming under imperfect channel state information (CSI) conditions at THz. UM-MIMO systems are promising for future 6G wireless communication networks due to their high spectral efficiency and capacity. The accuracy of CSI significantly influences the performance of UM-MIMO systems. However, acquiring perfect CSI is challenging due to various practical constraints such as channel estimation errors, feedback delays, and hardware imperfections. To address this issue, we propose a novel self-supervised contrastive learning-based approach for hybrid beamforming, which is robust against imperfect CSI. We demonstrate the power of contrastive learning to tackle the challenges posed by imperfect CSI and show that our proposed method results in improved system performance in terms of achievable rate compared to traditional methods.

关键词： 6G mobile communication Wireless communication Array signal processing Heuristic algorithms Conferences System performance Contrastive learning

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Two-Stage Guided Constraint Differential Evolution Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Reduced CAD system for classifications of cancer types based on microarray gene expression data 9

Reduced CAD system for classifications of cancer types based...

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9th IEEE International Conference on Sciences of Electronics, Technologies of information and Telecommunications, SETIT 2022

作者： Bacha, Sawssen Taouali, Okba Liouane, Noureddine National Engineering School of Monastir Research Laboratory of Automation Signal Processing and Image Monastir5019 Tunisia University of Tabuk Faculty of Computers and Information Technology Department of Computer Engineering Tabuk Saudi Arabia

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

Cancer is one of the deadliest diseases for human health. The classification of cancers poses many challenges in biomedical research because it allows an accurate and effective diagnosis and guarantees the success of medical treatments. In this paper, a new reduced computer-Aided Diagnosis (CAD) system is implemented under the MATLAB (version R2016a) environment to classifying four cancer subtypes. The results of the experiment are carried out with four sets of baseline data on the expression of cancer genes. To validate the proposed CAD system, different performance metrics such as sensitivity, specificity, accuracy, and F-Score are measured. The experimental analysis justifies the effectiveness of the proposed model and, therefore, this model can be considered as an effective tool to help radiologists for a better diagnosis. © 2022 IEEE.

关键词： Gene expression

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An improved KPLS-KELM method for breast cancer detection 9

An improved KPLS-KELM method for breast cancer detection

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9th IEEE International Conference on Sciences of Electronics, Technologies of information and Telecommunications, SETIT 2022

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

The conception of a computer-Aided Diagnosis system (CAD) using Artificial Intelligence (AI) is a hot topic in the domain of medical diagnosis. Recently, many approaches have been developed. In the proposed work, a novel classification technique from mammograms based on Kernel Extreme Learning Machine (KELM) and Kernel Partial Least Square (KPLS) method is introduced. The suggested algorithm first used the KPLS algorithm to extract features from the images. The extracted characteristics were then sent to the KELM classifier. In order to improve the generalization of the proposed approach, the cross-validation strategy was used. The simulation results were tested on the Mammographic Image Analysis Society (MIAS) dataset and measured using accuracy, F score, sensitivity, and specificity analysis. These results were compared to existing approaches tested on the same dataset and it was observed that the proposed work is the most efficient. © 2022 IEEE.

关键词： Mammography

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